Abstract
The Brazilian population increase and the purchase power growth have resulted in a widespread use of electric home appliances. Consequently, the demand for electricity has been growing steadily in an average of 5% a year. In this country, electric demand is supplied predominantly by hydro power. Many of the power plants installed do not operate efficiently from water consumption point of view. Energy Dispatch is defined as the allocation of operational values to each turbine inside a power plant to meet some criteria defined by the power plant owner. In this context, an optimal scheduling criterion could be the provision of the greatest amount of electricity with the lowest possible water consumption, i.e. maximization of water use efficiency. Some power plant operators rely on "Normal Mode of Operation" (NMO) as Energy Dispatch criterion. This criterion consists in equally dividing power demand between available turbines regardless whether the allocation represents an efficient good operation point for each turbine. This work proposes a multiobjective approach to solve electric dispatch problem in which the objective functions considered are maximization of hydroelectric productivity function and minimization of the distance between NMO and "Optimized Control Mode" (OCM). Two well-known Multiobjective Evolutionary Algorithms are used to solve this problem. Practical results have shown water savings in the order of million m(3)/s. In addition, statistical inference has revealed that SPEA2 algorithm is more robust than NSGA-II algorithm to solve this problem.

Abstract
This paper evaluates the flexibility provided by distributed energy resources (DER) in a real electricity distribution network in Germany. Using the Interval Constrained Power Flow (ICPF) tool, the maximum range of flexibility available at the primary substation was obtained for different operation scenarios. Three test cases were simulated, differing mainly in the considered level of renewable energy sources (RES) production. For each test case, the obtained results enabled the construction of flexibility areas that define, for a given operating point, the limits of feasible values for the active and reactive power that can be exchanged between the TSO and the DSO. Furthermore, the tool can also be used to evaluate the contribution from each type of DER to the overall distribution network flexibility.

Abstract
The uncertainty associated with the increasingly wind power penetration in power systems must be considered when performing the traditional day-ahead scheduling of conventional thermal units. This uncertainty can be represented through a set of representative wind power scenarios that take into account the time-dependency between forecasting errors. To create robust Unit Commitment ( UC) schedules, it is widely seen that all possible wind power scenarios must be used. However, using all realizations of wind power might be a poor approach and important savings in computational effort can be achieved if only the most representative subset is used. In this paper, the new hybrid metaheuristic DEEPSO and clustering techniques are used in the traditional stochastic formulation of the UC problem to investigate the robustness of the UC schedules with increasing number of wind power scenarios. For this purpose, expected values for operational costs, wind spill, and load curtailment for the UC solutions are compared for a didactic 10 generator test system. The obtained results shown that it is possible to reduce the computation burden of the stochastic UC by using a small set of representative wind power scenarios previously selected from a high number of scenarios covering the entire probability distribution function of the forecasting uncertainty.

Abstract
In this paper, a novel hybrid single-objective metaheuristic, the so called C-DEEPSO (Canonical Differential Evolutionary Particle Swarm Optimization), is proposed and tested. C-DEEPSO can be viewed as an evolutionary algorithm with recombination rules borrowed from PSO, or a swarm optimization method with selection and self-adaptiveness properties proper from DE. A case study on the problem of optimal control for reactive sources in energy production by Wind Power Plants (WPP), solved by means of Optimal Power Flow (OPF-like), is used to test the new hybrid algorithm and to evaluate its performance. C-DEEPSO is compared to the baseline algorithm, DEEPSO, and to a reference algorithm, Mean-Variance Mapping Optimization (MVMO). The experiments indicate that the proposed algorithm is efficient and competitive, capable to tackle this large-scale problem. The results also show that the new approach exhibits better results, when compared to MVMO.

Abstract
The growing integration of renewable energy in power systems demands for adequate planning of generation systems not only to meet long-term capacity requirements hut also to cope with sudden capacity shortages that can occur during system operation. As a matter of fact, system operators must schedule an adequate amount of operational reserve to avoid capacity deficits which can be caused by, for instance, overestimating the wind power that will be available. The framework proposed for the long-term assessment of operational reserve relies on the Nave forecasting method to produce wind power forecasts for the next hour. This forecasting model is simple and widely used to obtain short-term forecasts. However, it has been shown that regression models, such as the Autoregressive Integrated Moving Average (ARIMA) model, can outperform the Naive model even for forecasting horizons of up to 1 hour. This paper investigates the differences in the risk indices obtained for the long-term operational reserve when using the Naive and the ARIMA forecasting models. The objective is to assess the impact of the forecasting error in the long-term operational reserve risk indices. Experiments using the Sequential Monte Carlo Simulation (SMCS) method were carried out on a modified version of the IEEE RTS 79 test system that includes wind and hydro power variability. A sensitivity analysis was also performed taking into account several wind power integration scenarios and two different merit orders for scheduling generating units.

Abstract
The secure integration of renewable generation into modern power systems requires an appropriate assessment of the security of the system in real-time. The uncertainty associated with renewable power makes it impossible to tackle this problem via a brute-force approach, i.e. it is not possible to run detailed online static or dynamic simulations for all possible security problems and realizations of load and renewable power. Intelligent approaches for online security assessment with forecast uncertainty modeling are being sought to better handle contingency events. This paper reports the platform developed within the iTesla project for online static and dynamic security assessment. This innovative and open-source computational platform is composed of several modules such as detailed static and dynamic simulation, machine learning, forecast uncertainty representation and optimization tools to not only filter contingencies but also to provide the best control actions to avoid possible unsecure situations. Based on High Performance Computing (IIPC), the iTesla platform was tested in the French network for a specific security problem: overload of transmission circuits. The results obtained show that forecast uncertainty representation is of the utmost importance, since from apparently secure forecast network states, it is possible to obtain unsecure situations that need to be tackled in advance by the system operator.